An Introduction to Propensity Score Methods for Reducing the Effects of Confounding in Observational Studies
TLDR
The propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects, and different causal average treatment effects and their relationship with propensity score analyses are described.Abstract:
The propensity score is the probability of treatment assignment conditional on observed baseline characteristics. The propensity score allows one to design and analyze an observational (nonrandomized) study so that it mimics some of the particular characteristics of a randomized controlled trial. In particular, the propensity score is a balancing score: conditional on the propensity score, the distribution of observed baseline covariates will be similar between treated and untreated subjects. I describe 4 different propensity score methods: matching on the propensity score, stratification on the propensity score, inverse probability of treatment weighting using the propensity score, and covariate adjustment using the propensity score. I describe balance diagnostics for examining whether the propensity score model has been adequately specified. Furthermore, I discuss differences between regression-based methods and propensity score-based methods for the analysis of observational data. I describe different causal average treatment effects and their relationship with propensity score analyses.read more
Citations
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[The REporting of studies Conducted using Observational Routinely-collected health Data (RECORD) statement].
Eric I Benchimol,Liam Smeeth,Astrid Guttmann,Katie Harron,David Moher,Irene Petersen,Henrik Toft Sørensen,Erik von Elm,Sinead Langan +8 more
TL;DR: This document contains the checklist and explanatory and elaboration information to enhance the use of theRECORD checklist, and examples of good reporting for each RECORD checklist item are also included herein.
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Moving towards best practice when using inverse probability of treatment weighting (IPTW) using the propensity score to estimate causal treatment effects in observational studies
TL;DR: A suite of quantitative and qualitative methods are described that allow one to assess whether measured baseline covariates are balanced between treatment groups in the weighted sample to contribute towards an evolving concept of ‘best practice’ when using IPTW to estimate causal treatment effects using observational data.
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6-month neurological and psychiatric outcomes in 236 379 survivors of COVID-19: a retrospective cohort study using electronic health records.
Maxime Taquet,Maxime Taquet,John R. Geddes,John R. Geddes,Masud Husain,Sierra Luciano,Paul Harrison,Paul Harrison +7 more
TL;DR: In this article, the authors provided robust estimates of incidence rates and relative risks of neurological and psychiatric diagnoses in patients in the 6 months following a COVID-19 diagnosis, using data obtained from the TriNetX electronic health records network (with over 81 million patients).
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The use of propensity score methods with survival or time-to-event outcomes: reporting measures of effect similar to those used in randomized experiments
TL;DR: Two different propensity score methods (matching and inverse probability of treatment weighting) can be used to estimate the measures of effect that are frequently reported in randomized controlled trials with time‐to‐event outcomes: marginal survival curves and marginal hazard ratios.
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Bidirectional associations between COVID-19 and psychiatric disorder: retrospective cohort studies of 62 354 COVID-19 cases in the USA
Maxime Taquet,Maxime Taquet,Sierra Luciano,John R. Geddes,John R. Geddes,Paul Harrison,Paul Harrison +6 more
TL;DR: Survivors of COVID-19 appear to be at increased risk of psychiatric sequelae, and a psychiatric diagnosis might be an independent risk factor for COIDs, according to a preliminary study using data from 69 million patients.
References
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Journal ArticleDOI
The central role of the propensity score in observational studies for causal effects
TL;DR: The authors discusses the central role of propensity scores and balancing scores in the analysis of observational studies and shows that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
Journal ArticleDOI
Estimating causal effects of treatments in randomized and nonrandomized studies.
TL;DR: A discussion of matching, randomization, random sampling, and other methods of controlling extraneous variation is presented in this paper, where the objective is to specify the benefits of randomization in estimating causal effects of treatments.
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A simulation study of the number of events per variable in logistic regression analysis.
Peter Peduzzi,John Concato,John Concato,Elizabeth Kemper,Elizabeth Kemper,Theodore R. Holford,Alvan R. Feinstein,Alvan R. Feinstein +7 more
TL;DR: Findings indicate that low EPV can lead to major problems, and the regression coefficients were biased in both positive and negative directions, and paradoxical associations (significance in the wrong direction) were increased.
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Constructing a Control Group Using Multivariate Matched Sampling Methods That Incorporate the Propensity Score
TL;DR: This article used multivariate matching methods in an observational study of the effects of prenatal exposure to barbiturates on subsequent psychological development, using the propensity score as a distinct matching variable.
Journal ArticleDOI
Propensity score methods for bias reduction in the comparison of a treatment to a non‐randomized control group
TL;DR: The propensity score, defined as the conditional probability of being treated given the covariates, can be used to balance the variance of covariates in the two groups, and therefore reduce bias as mentioned in this paper.